如何使用numpy向量化Floyd-Steinberg的抖动算法？

import numpy as np

from PIL import Image

image = Image.open("image.jpg")  # 替换为待处理图像的路径
image = np.array(image)

palette = np.array([[0, 0, 0], [255, 255, 255]])  # 替换为自定义的调色板颜色

distances = np.sum((image[:, :, np.newaxis, :] - palette[np.newaxis, np.newaxis, :, :]) ** 2, axis=3)
indices = np.argmin(distances, axis=2)

errors = image - palette[indices]
errors[:, :-1] += errors[:, 1:] * 7 / 16
errors[1:, :-1] += errors[:-1, 1:] * 3 / 16
errors[1:, :] += errors[:-1, :] * 5 / 16
errors[1:, 1:] += errors[:-1, :-1] * 1 / 16

output_image = palette[indices]

output_image = Image.fromarray(output_image.astype('uint8'))
output_image.save("output_image.jpg")  # 替换为输出图像保存路径